AI is changing how people buy
The customer journey has never been linear or simple. But as more people use AI for product discovery, what used to often begin with a simple search query now starts as a conversation with a LLM.
60% of global consumers interact with AI at least weekly, according to Klaviyo’s 2026 AI Consumer Trends Report . Whether they’re making personal decisions, getting inspiration, or planning activities, at least 1 in 5 consumers begin their research with LLMs.
But the AI customer journey extends beyond product discovery. AI is also changing what consumers know before they land on your website and what they expect from the customer experience when they get there.
The customer journey has new friction points and new “aha” moments, and that means new opportunities for your brand to acquire new customers and retain existing ones who came to your website through AI.
Here’s what you need to know about the AI customer journey, so your brand can see the benefits of AI shopping.
In this guide:
- Collapsed discovery and research
- Extended consideration phase
- Real-time answers for conversion
- Personalization trust signals
- Poor AI and brand trust
- Customer service revenue
Shift 1: AI has collapsed the discovery and research phases
Before AI shopping, most B2C product discovery happened through traditional organic search or social media. When the customer entered the research phase, they’d most likely hop through several websites or social media accounts to compare products.
Now, AI is condensing this process. With AI conversations, consumers are discovering and comparing new products within the same chat interface. In some cases, they can even buy a product without leaving the AI conversation.
43% of consumers have used AI when shopping for non-essential products in the past 6 months, according to our AI consumer trends research. 39% have purchased a product recommended by AI during that time period, too.
When searching with AI, consumers also use longer, more detailed prompts to find what they’re looking for. According to our AI Consumer Trends Report, 30% of consumers use 8+ keywords for an AI search query, and 78% say they include emotional or personal context at least some of the time.
With this context, LLMs can recommend product listings, generate comparisons, and summarize customer reviews rather than surfacing a list of brand websites. That means the consumer doesn’t need to open another tab to move on to the research phase of their customer journey.
LLMs can also follow up with clarifying questions, like, “Would you like me to share free shipping or in-person pick-up options?” or “Is there a particular color or style you prefer?” This is what makes it possible for the customer to discover and research products in the same conversation.
How to adapt
- Structure your content to match AI search habits. Keyword-optimized product copy alone won't lead to product discovery through AI. Organize product descriptions and metadata around use cases, comparisons, scenarios, and outcomes—not just categories and specs.
- Gather more detailed customer reviews. LLMs are starting to cite third-party customer review websites as leading sources of information when surfacing product recommendations, according to Search Atlas research . Develop a more thoughtful strategy for collecting and incentivizing an abundance of new, detailed reviews , so that AI crawlers always have current ones to pull from.
Shift 2: The consideration phase is longer
AI can curate product recommendations and build comparison tables in seconds. The irony is that, with so much information instantly available, consumers are spending more time weighing their options.
2025 was the year of the “ browsing boom ” for ecommerce brands. Product views across ecommerce sites were up 37% YoY pre-BFCM, while order growth rose 14%—a widening gap that signals shoppers are taking their time to compare, research, and consider before they buy.
This jump can’t be explained entirely by AI shopping, especially as more shoppers are viewing product listings in AI platforms, not on-site. But other research shows a correlation between AI and consideration: in a 2026 survey by Gartner , for example, 31% of consumers reported that AI overviews lead them to think about more product options.
AI is a shortcut to finding the best products based on contextual need. But consumers still take time to digest the details before taking the next step, and they may have more detailed conversations with LLMs as they do.
This means shoppers may be more informed and have higher expectations when they land on your website to dig even deeper. If the on-site experience doesn't match these expectations, you risk losing a high-intent visitor.
How to adapt
- Set up AI attribution. Shopify and Klaviyo data use UTM parameters to help you see who started a check-out, placed an order, or browsed but didn’t buy from an AI search. You can use this data to deliver personalized abandonment and post-purchase flows with more detailed product information, since you can reasonably assume AI shoppers want it.
- Invest in detailed, contextual zero-party data collection. Learn as much as you can about your AI traffic audience, so you can build a good relationship with them and hopefully shorten their consideration phase. Use quizzes or forms to gather more in-depth preference or lifestyle data, so your browse and cart abandonment flows are meeting their contextual needs.
Shift 3: Conversion requires real-time answers
Similar to how Amazon created new expectations about fast delivery, AI is creating new expectations about instant, in-depth access to information.
People don’t suspend these expectations when they land on your website. In fact, 75% of consumers have abandoned a purchase because they couldn't get instant answers, according to the research behind Klaviyo’s 2025 AI Shopping Index .
Brand-side AI customer agents are becoming a requirement for the AI customer journey. But these interactions need to deliver the same value consumers are used to with LLMs—and that means they need to be personalized.
An AI customer agent should be able to:
- Recommend products based on real-time customer data, including past browsing behavior, purchases, and stated preferences.
- Suggest products that pair well with items already in someone’s shopping cart.
- Answer detailed questions about each product in your inventory, including what they’re made from, where they’re sourced, or when they’re anticipated to be back in stock.
How to adapt
- Train your AI customer agent on customer and product data. Your AI agent won’t be able to offer high-quality, personalized answers to questions if it knows nothing about your customers or your products. Personalized service means your AI agent has a dynamic, growing knowledge of your customers and products at all times.
- Make it easy to escalate complex inquiries to a human. There will always be certain questions that even the most capable AI agent can’t resolve by itself. Use a shared helpdesk where your AI and human agents have access to the same data and context, so the hand-off doesn’t cause friction for the customer.
Shift 4: Personalization can make or break trust
74% of consumers expect more personalized experiences from brands, according to Klaviyo’s 2025 Future of Consumer Marketing Report .
This was true before AI shopping became more common, but AI personalization is also starting to make or break trust between brands and consumers: more than half (55%) of consumers have had an “aha” moment where AI impressed them with accuracy or personalization, according to our AI consumer trends research.
When personalization fails, the consequences compound. The same research found that consumers’ top reaction to receiving poorly personalized content is to be less likely to open future messages from that brand.
How to adapt
- Unify your customer data. Your personalization strategy is only as strong as your customer data. If your customer profiles are incomplete or fragmented across multiple platforms, now is the time to consider consolidation. A shared, real-time view of each customer makes it easier to deliver consistent, accurate personalization across every marketing and customer service touchpoint.
- Invest in predictive analytics. Use predictive analytics to move beyond rule-based personalization. Instead, tailor messages to individual timing, channel, and product preferences. For instance, you could send well-timed discounts with personalized product recommendations to high-risk customers by combining their browsing behavior, channel preference, preferred send time, and potential churn risk.
Shift 5: Brands can lose trust when they implement AI poorly
Only 13% of consumers completely trust AI, according to our AI Consumer Trends Report. This forces brands to operate in a gray area: as shoppers acclimate to AI and form their own opinions about how to use it, AI can be a benefit and a burden.
The same research found that almost half (47%) of consumers think AI has improved the quality of product recommendations they receive. 43% say it’s transforming brands’ customer service for the better.
But a recommendation that misses the mark, a robotic support interaction, or generic AI content can quickly drive people away: nearly 1 in 5 consumers lose trust in a brand's data practices after a poorly personalized experience, according to our research, and 41% say customer service chats that don’t feel human are the brand interactions that come off as “too automated.”
How to adapt
- Keep humans in the loop. Every consumer is at a different point with AI, so don’t force the technology on skeptics. When you do offer AI experiences, make it clear how shoppers can switch or escalate the conversation to a human.
- Don’t sacrifice your brand style. Train your AI on your brand’s distinct voice and tone guidelines to prevent AI interactions from feeling cold or generic.
- Monitor AI-specific trust signals. Keep tabs on where AI is harming the customer experience. Track metrics like unsubscribe rates after AI-generated marketing messages, survey responses after AI agent interactions, and sentiment throughout AI customer agent conversations.
Shift 6: Customer service is a revenue generator
AI is shifting post-purchase customer service from a returns and order management function to a proactive revenue and loyalty driver. When AI agents are available 24/7, every interaction after an initial sale is an opportunity to deepen the customer relationship and generate repeat revenue.
AI agents that have access to real-time customer data can deliver a white glove experience. For example:
- A hotel brand’s AI agent could proactively offer local restaurant recommendations or assist with transportation plans after a guest books their stay.
- A children’s retailer could set up an AI agent to analyze customers’ baby registry information and suggest age-appropriate items based on their child’s predicted age or size.
How to adapt
- Sync service and marketing data. Every customer interaction generates data that can personalize retention touchpoints. Your marketing and customer service data should be accessible from the same place, so that AI and human agents can tailor their conversations to previously shared preferences and product questions.
- Create a 1:1 post-purchase experience. Set up a self-serve customer hub where shoppers can do more than track their orders. Offer personalized product feeds, AI agents that answer questions and recommend products, and a place where customers can redeem loyalty points.
- Anticipate customers’ needs before they even ask. Turn purchase history into more proactive, relevant customer outreach. Use AI to predict the next best products a customer might want based on their previous orders, for example, and embed those recommendations in post-purchase emails or in customer hubs.
How Klaviyo supports the AI customer journey
The customer journey still includes awareness, consideration, conversion, fulfillment, and loyalty phases. What’s changed is the infrastructure underneath it.
AI has made discovery faster, consideration more thorough, expectations higher, and the cost of friction steeper.
Here’s how Klaviyo can help you adapt your marketing and service for the AI customer journey:
- Nurture: Composer plans and launches marketing autonomously, based on a simple prompt or idea and your brand guidelines. You maintain full strategic control—nothing launches without your sign-off.
- Conversion: K:AI Customer Agent draws on your customer data, product catalog, policies, and brand voice to provide 24/7 personalized shopping assistance, answer questions, and escalate issues to humans with full context intact.
- Personalization: Features like predictive analytics, channel affinity, and personalized A/B testing deliver 1:1 personalization without manual segmentation.
- Post-purchase: Klaviyo Customer Hub creates one personalized place where customers can manage their relationship with your brand, from self-service options to product recommendations and rewards redemption.